CN115795399B - Multi-source remote sensing precipitation data self-adaptive fusion method and system - Google Patents
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Abstract
The application relates to the technical field of methods or devices for identifying by using electronic equipment, and provides a multi-source remote sensing precipitation data self-adaptive fusion method and system. According to the method, the weight corresponding to each rainfall data is self-adaptively adjusted based on the error characteristics of the multi-source remote sensing rainfall data, the self-adaptive characteristic fusion data of the multi-source remote sensing rainfall data is obtained through calculation based on the weight, then the rainfall data after the self-adaptive characteristic fusion is optimized and downscaled by combining the rainfall influence factors, the downscaled result of the self-adaptive characteristic fusion data of the multi-source remote sensing rainfall data is obtained, finally, the downscaled result is used as an initial condition of the HASM method after parameter optimization, the observed value of a meteorological site is used as an optimal control condition, and the multi-source rainfall fusion model is constructed. The fusion model breaks through the limitation that the traditional precipitation data fusion model needs to be established on a certain premise assumption, and can obtain the optimal estimation of the precipitation space distribution with high space-time resolution and small uncertainty.
Description
Technical Field
The application relates to the technical field of methods or devices for identifying by using electronic equipment, in particular to a multi-source remote sensing precipitation data self-adaptive fusion method and system.
Background
Precipitation is an important component of energy exchange and water circulation in a climate system, is an important index for representing climate change, and has very important influence on human activities and social and economic development. The high-quality precipitation space-time distribution information has important significance for researching the processes of climate, weather, ecology, hydrology and the like. Meanwhile, as essential basic data in multidisciplinary cross fusion research such as atmospheric science, hydrology, geography, ecology and the like, precipitation data on a fine space-time scale is an important driving parameter of various research models, and the estimation accuracy of the precipitation data has very important influence on the simulation result of the research models. The Chinese amplitude-man is wide, the eastern Asia monsoon area at the ground spans a plurality of climate zones, and is influenced by various factors such as sea-land position, topography, monsoon, underlying surface, human activity and the like, the precipitation presents complex space-time variation characteristics, and particularly the daily precipitation process presents obvious randomness and space-time difference. The accurate acquisition of the space-time characteristic information of precipitation is an important basis for the works of hydrologic and water resource management, flood drought detection, geological disaster early warning, risk assessment and the like.
With the rapid development of a meteorological observation system, more and more data are acquired by using ground meteorological stations, radars, satellites and the like, and the continuous progress of technical methods, mass multi-source multi-scale precipitation data are accumulated at present, the spatial-temporal resolutions of the precipitation data are different, and different precision characteristics are shown for the precipitation in the same area. At present, integration of precipitation observation information or estimation information with different sources, different accuracies and different time-space resolutions through a certain optimization criterion to obtain high-accuracy fine time-space scale precipitation spatial distribution data is a leading problem and a scientific difficulty in the field of global change research, and has great development potential.
The concept of multi-source precipitation data fusion has been introduced into quantitative estimation of precipitation space since the 90 s of the last century, and an important thought is provided for estimating precipitation space distribution based on multi-source information. The data fusion has the advantages of wide space-time coverage, high reliability, reduced uncertainty of data information, improved space-time resolution of data and the like, and becomes an important means for acquiring the same target information by multi-source data. Under the framework of precipitation fusion, precipitation data of different source properties such as ground observation or remote sensing measurement are integrated into a quantitative model, and more reasonable estimation of the real distribution state of precipitation is obtained through advantage complementation and reasonable matching.
At present, students at home and abroad sequentially develop a series of fusion researches on satellite-ground multi-source precipitation data, and common fusion methods comprise an objective analysis method, a probability density method, an optimal weight method, a conditional fusion method, a ground statistics method, a Bayesian estimation method, a machine learning-based method and the like. The fusion methods are combined with a specific fusion mode through a certain precondition assumption condition to obtain the optimal estimation of the real distribution of the precipitation, however, the fusion method is insufficient in consideration of space-time variation characteristics of the precipitation data, and also insufficient in consideration of different precision characteristics of the precipitation data in the same area, so that the precision of the fusion model still has a certain improvement space.
Therefore, it is necessary to provide a technical solution that can fully utilize the advantages of more different data sources to obtain precipitation spatial distribution information with high spatial-temporal resolution and small uncertainty.
Disclosure of Invention
The utility model aims at providing a multisource remote sensing precipitation data self-adaptation fuses method and system, this system fully considers the different precision characteristic of same regional precipitation data, can give full play to the advantage of multiple different data sources, obtains high-accuracy meticulous space-time scale precipitation space distribution data from precipitation observation information or the estimated information of different sources, different precision, different time space resolution.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a multi-source remote sensing precipitation data self-adaptive fusion method, which comprises the following steps:
based on error characteristics of multi-source remote sensing precipitation data, calculating and obtaining weight corresponding to precipitation data of each data source in the multi-source remote sensing precipitation data by using a Lagrange multiplier method;
calculating to obtain self-adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data;
Performing downscaling on the adaptive feature fusion data of the multi-source remote sensing precipitation data by using a geographic weighted ridge regression method and combining influence factors of precipitation to obtain a downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data;
and constructing a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance by combining an improved high-precision curved surface modeling method.
Preferably, the method for applying a geographic weighted ridge regression combines influence factors of precipitation to downscale the adaptive feature fusion data of the multi-source remote sensing precipitation data to obtain downscale results of the adaptive feature fusion data of the multi-source remote sensing precipitation data, specifically:
downscaling the adaptive feature fusion data of the multi-source remote sensing precipitation data;
in the method, in the process of the invention,
a regression function constructed for a geographic weighted ridge regression method;vdata are fused for the self-adaptive characteristics of the multi-source remote sensing precipitation data;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 and merging the downscaling result of the data for the self-adaptive characteristic of the multi-source remote sensing precipitation data.
Preferably, the expression of the multi-source precipitation fusion model is as follows:
wherein:A、B、Ccoefficient terms of a finite difference equation set corresponding to the high-precision curved surface modeling method;d、q、pthe right end term of the finite difference equation set corresponding to the high-precision curved surface modeling method is used;x n+1 representing the grid points on the simulated surface corresponding to the high-precision surface modeling methodn+1The value of the secondary iteration;Sis a sampling matrix;gis a sampling vector;
a regression function constructed for a geographic weighted ridge regression method;vdata are fused for the self-adaptive characteristics of the multi-source remote sensing precipitation data;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 the self-adaptive characteristics of the multisource remote sensing precipitation data are fused with the downscaling result of the data to serve as a precipitation space distribution initial curved surface corresponding to the current iteration of the high-precision curved surface modeling method;H、Lthe method is respectively the upper and lower boundaries of each grid point on the simulated curved surface corresponding to the high-precision curved surface modeling method.
Preferably, the expression of the error characteristic of the multi-source remote sensing precipitation data is as follows:
wherein:σ 2 is the mean square error;Erepresenting expectationsA value;urepresenting the actual precipitation data,u i represent the firstiPrecipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source; vSelf-adaptive feature fusion data representing the multi-source remote sensing precipitation data;krepresenting the total number of data sources.
Preferably, based on the error characteristics of the multi-source remote sensing precipitation data, the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data is calculated by using a Lagrange multiplier method, and specifically:
solving an expression of the error characteristic of the multi-source remote sensing precipitation data by using a Lagrangian multiplier method to obtain a weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data, wherein the expression of the weight is as follows:
wherein:
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source;krepresenting the total number of data sources.
Preferably, the expression of the adaptive feature fusion data of the multi-source remote sensing precipitation data is as follows:
wherein:vself-adaptive feature fusion data representing the multi-source remote sensing precipitation data;
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;u i represent the firstiPrecipitation data of the data source;krepresenting the total number of data sources.
Preferably, after constructing the multi-source precipitation fusion model according to the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance and combining with the improved high-precision curved surface modeling method, the method further comprises:
Solving the multisource precipitation fusion model by adopting a pretreatment conjugate gradient method and multiple iteration steps, and continuously adjusting the precipitation space distribution initial curved surface under the optimization control constraint of the meteorological site observation datax 0 And finally obtaining the optimal estimation value of the precipitation space distribution.
Preferably, the method further comprises: for each iteration, each grid point on the simulated surface is processed as follows:
if no weather station exists in the current grid point, determining the upper and lower boundaries of the current grid point according to the relaxation coefficient of the high-precision curved surface modeling method and the extreme value of the adjacent grid point in the searching radius of the high-precision curved surface modeling methodH、L；
Wherein the searching radius is the upper and lower bounds of the current grid point determined by a high-precision curved surface modeling methodH、LThe adjacent grid points to be searched;
if the number of weather stations in the current grid point is less than the preset threshold value, the value on the adjacent grid point in the searching radius is defined as the average value of the observed value of the existing weather stations in the radius and the grid point value of the multi-source remote sensing precipitation data in the searching radius, and the average value is the same asx n+1 Satisfy inequality
。
Preferably, for each iteration, the sampling point weight corresponding to each meteorological site is determined by the following steps:
Calculating the average value of adjacent grid point values of the positions of all weather stations on the current iteration simulation curved surface;
and calculating the difference between the observed data of each meteorological site and the average value, and taking the calculated difference as the weight of the sampling point corresponding to each meteorological site.
The embodiment of the application also provides a multisource remote sensing precipitation data self-adaptive fusion system, which comprises:
the weight calculation unit is configured to calculate and obtain the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data by utilizing a Lagrange multiplier method based on the error characteristics of the multi-source remote sensing precipitation data;
the self-adaptive feature fusion unit is configured to calculate and obtain self-adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data;
the data optimization unit is configured to apply a geographic weighted ridge regression method, and combine influence factors of precipitation to downscale the adaptive feature fusion data of the multi-source remote sensing precipitation data to obtain a downscaled result of the adaptive feature fusion data of the multi-source remote sensing precipitation data;
The model construction unit is configured to construct a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and weather site observation data acquired in advance and an improved high-precision curved surface modeling method.
The beneficial effects are that:
according to the technical scheme, based on the error characteristics of the multi-source remote sensing precipitation data, the Lagrange multiplier method is utilized to calculate the weight corresponding to the precipitation data; and calculating to obtain self-adaptive feature fusion data based on the weight and the multisource remote sensing precipitation data; then, a geographic weighted ridge regression method is used, and the adaptive feature fusion data of the multi-source remote sensing precipitation data are downscaled by combining the influence factors of precipitation, so that a downscaled result of the adaptive feature fusion data of the multi-source remote sensing precipitation data is obtained; and constructing a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance by combining an improved high-precision curved surface modeling method. According to the method, the weight corresponding to each data source can be adjusted in a self-adaptive mode according to the error characteristics of the fused precipitation data of the multiple sources, the high-precision simulation advantage of the high-precision curved surface modeling method is fully utilized, a multi-source precipitation fusion model capable of fusing the high-dimensional precipitation data is constructed, the model breaks through the limitation that the existing precipitation data fusion model needs to be built on a certain premise assumption, and meanwhile breaks through the limitation that the existing precipitation data fusion model is limited to two to three sources, and the multiple sources (three or more) and multiple scales of precipitation data can be fused effectively, so that the high-precision fusion method is provided for the high-dimensional, multi-source and multi-scale precipitation data.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. Wherein:
fig. 1 is a flow chart of a method for adaptive fusion of multi-source remote sensing precipitation data according to some embodiments of the present application;
FIG. 2 is a logic diagram of a multi-source remote sensing precipitation data adaptive fusion method provided in accordance with some embodiments of the present application;
fig. 3 is a schematic structural diagram of a multi-source remote sensing precipitation data adaptive fusion system according to some embodiments of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. Various examples are provided by way of explanation of the present application and not limitation of the present application. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment. Accordingly, it is intended that the present application include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
As described in the background art, the current common fusion method for satellite-ground multisource precipitation data comprises the following steps: objective analysis, probability density, optimal weight, conditional fusion, geostatistical, bayesian estimation, machine learning based methods, etc., where the assumption conditions and specific fusion methods of these fusion methods are different, but the basic ideas are the same: most of the methods are established under the premise of a certain premise, by constructing a background field of precipitation data, an optimization scheme is adopted to correct the background field in combination with ground actual measurement data, and then the optimal estimation of the real distribution of the precipitation is obtained. Because the fusion methods are established under a certain premise, certain uncertainty is brought to the model.
In addition, the current research on fusion of multi-source precipitation data is mostly based on fusion of two or three data products in site and remote sensing data or mode results by adopting different methods, and the research on high-precision fusion of data products with more than three sources is less, so that the current massive multi-source and multi-scale precipitation estimation products are not fully and effectively utilized. In addition, most of the current fusion models do not consider error characteristics of precipitation data from different sources, so that high-precision fusion can be performed by more accurately and effectively utilizing different data characteristics.
With the rapid development of a weather observation system, more and more rainfall data are acquired by using ground weather stations, radars, satellites and the like, the quality of various numerical mode simulation data is continuously improved, the advantages of various different data sources are fully brought into play by combining with multidisciplinary research ideas under the condition of rapid growth of the rainfall data scale, an effective fusion method of the multisource multiscale rainfall data is researched, so that precipitation space distribution information with high space-time resolution and small uncertainty is acquired, the theoretical method framework of the current rainfall simulation is facilitated to be enriched and developed, effective data support can be provided for smooth implementation of regional disaster prevention and reduction, reasonable development and utilization of water resources, climate change evaluation and the like, and the method reference can be provided for fusion research of other geographic environment variables.
Therefore, the application provides a multi-source remote sensing precipitation data self-adaptive fusion method and system. The method can be used for high-precision fusion of high-dimensional data according to the error characteristics of the multi-source remote sensing precipitation data, can be used in the fields of space distribution simulation of elements such as climate elements, ecological environment elements and geographic topography under a big data background, can be regarded as a method for curved surface grid approximation, and is used for multi-source curved surface approximation modeling in large-scale physical, chemical, medical and other aspects.
Exemplary method
The embodiment of the application provides a multi-source remote sensing precipitation data self-adaptive fusion method, as shown in fig. 1 and 2, comprising the following steps:
and step S101, calculating and obtaining the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data by utilizing a Lagrange multiplier method based on the error characteristics of the multi-source remote sensing precipitation data.
It should be noted that the multi-source remote sensing precipitation data may also be referred to as multi-source, multi-scale precipitation data. The multi-scale precipitation data can be precipitation data with different spatial resolutions or different time resolutions. In the process of precipitation data fusion, precipitation data of different sources are expressed by a plurality of data dimensions, wherein a high dimension can be understood as precipitation data from a plurality of sources, further, a high dimension can be understood as more than three data sources, and precipitation data of different sources are different in data structure.
In the embodiment of the application, the error characteristics of the multi-source remote sensing precipitation data can be expressed in various error modes, such as mean square error, root mean square error, mean square error and the like.
Specifically, when the error characteristics of the multi-source remote sensing precipitation data are expressed by mean square error, the acquired multi-source remote sensing precipitation data are assumed to be u i ，i=1,2，…k，kIs the number of sources of precipitation data, and the mean value and the variance of each precipitation data are respectivelye i 、σ i The data obtained after the self-adaptive characteristic fusion of the multisource remote sensing precipitation data is thatvThe mean square error after fusion can be expressed as:
wherein:σ 2 is the mean square error;Erepresenting the expected value;urepresenting the actual precipitation data,u i represent the firstiPrecipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of data source, and
；vself-adaptive feature fusion data representing multi-source remote sensing precipitation data;krepresenting the total number of data sources.
Taking the weight in the formula (1) as a formula, obtaining:
in order to obtain a weight expression, in the embodiment of the application, the lagrangian multiplier method is utilized to solve the expression of the error characteristics of the multi-source remote sensing precipitation data, so that the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data is obtained.
Specifically, the formula (2) is converted into the following functional form by using lagrangian (Lagrange) multiplier method:
in the method, in the process of the invention,λis Lagrange multiplier.
For the function of formula (3)ω i AndλRespectively deriving and setting the values to 0, the following equation set can be obtained:
then, solving the formula (4) and the formula (5) to obtain the expression of the weight as follows:
Wherein:
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source;krepresenting the total number of data sources.
Step S102, calculating to obtain self-adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data.
Specifically, based on the weight expression of the formula (6), the expression of the adaptive feature fusion data of the multi-source remote sensing precipitation data is obtained as follows:
wherein:vself-adaptive feature fusion data representing multi-source remote sensing precipitation data;
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;u i represent the firstiPrecipitation data of the data source;krepresenting the total number of data sources.
In the embodiment, the error characteristics of the precipitation data of each source are expressed firstly, then the weight of the precipitation data of each data source is calculated in a self-adaptive mode based on the fused error characteristics of each data source, and a foundation is laid for the subsequent construction of a high-precision multi-source precipitation fusion model.
And step S103, performing downscaling on the adaptive feature fusion data of the multi-source remote sensing precipitation data by using a geographic weighted ridge regression method and combining influence factors of precipitation to obtain a downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data.
The geo-weighted ridge regression method (geographically weighted ridge regression, GWRR) is a technique that uses ridge parameters to locally compensate a geo-weighted regression analysis model (GWR) to improve the accuracy of the GWR model and solve the problem of multiple collinearity of regression coefficients in the GWR model by limiting the range of regression parameters to shrink the influence caused by redundant interpretation variables.
In the embodiment of the application, a geographic weighted ridge regression method is applied, adaptive feature fusion data of multi-source remote sensing precipitation data are downscaled by combining influence factors of precipitation, and downscaled results of the adaptive feature fusion data of the multi-source remote sensing precipitation data are obtained, specifically:
according to the following expression:
downscaling adaptive feature fusion data of the multi-source remote sensing precipitation data;
in the method, in the process of the invention,
a regression function constructed for a geographic weighted ridge regression method;vthe self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data are obtained;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 and merging the downscaling result of the data for the self-adaptive characteristics of the multi-source remote sensing precipitation data.
Among other factors, the impact of precipitation may include cloud cover, cloud optical thickness, cloud particle effective radius, cloud top temperature, cloud top air pressure, cloud water path, 500hPa and 800hPa potential altitude, air temperature, latent heat flux, sensible heat flux, short wave radiation, long wave radiation, relative humidity, maximum relative humidity, minimum relative humidity, specific humidity (ground, 500hPa and 800 hPa), sea level air pressure, wind speed, elevation, slope, longitude, latitude, distance to coastline, vegetation normalization index NDVI, etc.
Step S104, constructing a multi-source precipitation fusion model according to a downscaling result of self-adaptive characteristic fusion data of multi-source remote sensing precipitation data and weather site observation data acquired in advance and combining an improved high-precision curved surface modeling method.
The meteorological site observation data are acquired through a ground meteorological site. The weather station is provided with various sensors for weather observation, which can observe weather element values of the atmosphere close to the ground and some phenomena in free atmosphere, and can collect weather data such as air temperature, air pressure, air humidity, wind direction and speed, cloud, visibility, weather phenomena, precipitation, evaporation, sunlight, snow depth, ground temperature and the like.
It should be noted that the high-precision curved surface modeling (High Accuracy Surface Modelling, abbreviated as HASM) method is a curved surface modeling method which is established based on differential geometry theory and optimization control theory, uses global approximation data (including remote sensing data and global model coarse resolution analog data) as a driving field, uses local high-precision data (including monitoring network data and survey sampling data) as an optimization control condition, solves the error problem and multiscale problem which plague curved surface modeling for half a century, and refines and forms the basic theorem of earth surface modeling on the basis of mass application in more than 20 years.
Specifically, according to the principle of the basis of the theory of the curved surface, a first basic quantity of the curved surface is setE、F、GAnd a second type of basic quantityL、 M、NThe symmetry is satisfied and the degree of freedom is,E、F、Gis positively fixed, and the position of the device is positively fixed,E、F、G、L、MandNmeeting Gaussian (Gauss) equation set, then the full differential equation set isf （x，y）=f（x 0 ，y 0 ），（x=x 0 ，y=y 0 ) Under initial conditions, there is a unique solutionz=f（x，y）。
The expression of the Gauss equation set is:
wherein:
，
wherein:f x 、f y respectively isfAt the position ofx、yThe first order of the bias of the direction,f xx 、f yy respectively isfAt the position ofx、yThe second order of deviation of the direction is used,f xy is thatfAt the position ofx、yThe mixed partial derivative of the direction is used,
is the second class of kristolochial symbols.
If it is
Is a calculation domain +.>
Orthogonal subdivision of->
Computing domain standardized for dimensionless form, +.>
To calculate step size,/->
For a grid of normalized computational domains (also called grid points), the finite difference approximation of the first class basis quantities is: />
The finite difference approximation of the second type of basis weight is:
the finite difference of the second class of kristolochial symbols can be expressed as:
the finite difference form of the Gauss equation set is:
the matrix form of equation (10) above can be written as:
wherein:
，
in connection with efficient constrained control of local high-precision data, such as monitoring network data and survey sampling data, the constrained least squares problem of equation (11) above can be expressed as the equation-constrained least squares problem solved by HASM, as shown in equation (12):
In the method, in the process of the invention,Sin order to sample the matrix of samples,gin order to sample the vector of the data,A、B、Ccoefficient terms of the HASM finite difference equation set;d、q、pright-hand terms of the HASM finite difference equation set if
Is->
In the first placemSampling point [ ]x i ，y j ) The value of (1)S m，（i-1）×J+j =1，
。
Thus, the HASM is ultimately converted to an equality constrained least squares problem constrained by ground sampling in order to keep overall simulation errors to a minimum while ensuring that the simulation value at the sampling point is equal to the sampling value. The method fully utilizes sampling information, and is an effective means for ensuring that the iteration approaches to the optimal simulation effect.
Using the French equation set method, the constrained least squares problem represented by equation (12) can be transformed into:
wherein,,
，θis the weight coefficient of the meteorological site. />
After the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data is obtained in the foregoing steps, the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data is used as an initial condition of the HASM based on the HASM method of the formula (12), meteorological site observation data (i.e. sampling data) is used as an optimal control condition, meanwhile, a high-order finite difference format is adopted at the boundary of the simulation area for discretization, and upper and lower boundary control is performed on each grid point on the simulation curved surface according to the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data, so that the expression of the multi-source precipitation fusion model is obtained as follows:
Wherein:A、B、Ccoefficient terms of a finite difference equation set corresponding to the high-precision curved surface modeling method;d、q、pthe right end term of the finite difference equation set corresponding to the high-precision curved surface modeling method is used;x n+1 representing the grid points on the simulated surface corresponding to the high-precision surface modeling methodn+1The value of the secondary iteration;Sis a sampling matrix;gis a sampling vector;
a regression function constructed for a geographic weighted ridge regression method;vthe self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data are obtained;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 the method is characterized in that a downscaling result of self-adaptive characteristic fusion data of multi-source remote sensing rainfall data is used as an initial rainfall space distribution curved surface corresponding to the current iteration of a high-precision curved surface modeling method;H、Lthe method is characterized in that the method is respectively obtained by contour control of upper and lower boundaries of grid points on a simulated curved surface corresponding to a high-precision curved surface modeling method.
It should be noted that, the conventional HASM is mostly used for site data interpolation research, and uses the effective information of the site to construct the simulated curved surface through the curved surface equation, which essentially belongs to an interpolation method. In the embodiment of the application, the advantages of high-precision simulation of the HASM are fully utilized, the HASM is combined with self-adaptive feature fusion data of multi-source remote sensing precipitation data, and a fusion model which can be used for effectively fusing high-dimensional, multi-source and multi-scale precipitation data is obtained, so that the advantages of various different data sources are fully exerted, and precipitation space distribution information with high space-time resolution and small uncertainty is obtained.
To solve the multi-source precipitation fusion model, in some embodiments, after constructing the multi-source precipitation fusion model according to the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance and combining with the improved high-precision curved surface modeling method, the method further includes: solving the multisource precipitation fusion model by adopting a pretreatment conjugate gradient method and multiple iteration steps, and continuously adjusting the precipitation space distribution initial curved surface under the optimization control constraint of meteorological site observation datax 0 Finally obtaining the optimal estimation value of the precipitation space distributionX (*) 。
It should be noted that, in the first iteration, the right-hand term in the basic equation set (i.e., the equation set represented by the equation (12)) is used as an interpolation method in the past by HASMd、q、pAll the initial values are initialized to be 0, namely the iteration initial values are zero when the numerical simulation is solved. In the multisource precipitation fusion model constructed by the application, namely the formula (14), the iteration specific numerical value of the initial conditionX (0) Initial curved surface spatially distributed by precipitationx 0 Calculated, precipitation space distribution initial curved surfacex 0 The method is obtained by carrying out geographic weighting on the downscaling result of the adaptive feature fusion data of the multisource remote sensing precipitation data and then combining the influence factors such as the geographic topography of precipitation, and the like, so that the method is used X (0) The model is solved as an iteration initial value of the HASM, so that the precision of the fusion result can be greatly improved.
The search radius, the upper and lower bounds of each grid point are important super parameters of the HASM method. In the prior art, when the HASM is iteratively solved, the search radius is usually set to a fixed value, and is set to 12 by default. In the embodiment of the application, the value of the search radius can be divided along with regional precipitationThe cloth heterogeneity is determined, and in the iterative solving process, the upper and lower boundaries of each grid point are determined by the following modes: for each iteration, each grid point on the simulated surface is processed as follows: if no weather station exists in the current grid point, determining the upper and lower boundaries of the current grid point according to the relaxation coefficient of the high-precision curved surface modeling method and the extreme value of the adjacent grid point in the searching radius of the high-precision curved surface modeling methodH、LThe method comprises the steps of carrying out a first treatment on the surface of the Wherein, the searching radius is the upper and lower boundaries of the current grid point determined by a high-precision curved surface modeling methodH、LThe adjacent grid points to be searched; if the number of weather stations in the current grid point is less than the preset threshold value, the value of the adjacent points in the searching radius is defined as the average value of the observed values of the existing weather stations in the radius and the grid point values of the multi-source remote sensing precipitation data in the searching radius, and meanwhile x n+1 Satisfy inequality
。
Specifically, in the embodiment of the application, considering that the number of meteorological stations is often limited, a relaxation coefficient with a value range of 0-1 is introduced, and in the iterative solving process, for grid points on a simulated curved surface without meteorological stations, such as areas with high altitude, unmanned areas, complex terrain and the like, the upper and lower boundaries of the grid point value are determined according to the relaxation of the extreme values of adjacent grid points in the searching radius; for grid points with the number of meteorological sites being less than a preset threshold, namely a site sparse area, the values on adjacent grid points in the searching radius are defined as average values of the observed values of the existing meteorological sites in the searching radius and the grid point values of the multi-source remote sensing precipitation data in the searching radius, and meanwhile inequality is satisfied:
. Therefore, the upper boundary and the lower boundary of each grid point are constrained according to the multisource remote sensing precipitation data, and the solving precision of the fusion model is further improved.
In some embodiments, for each iteration, the sampling point weight corresponding to each meteorological site is determined by: calculating the average value of adjacent grid point values of the positions of all weather stations on the current iteration simulation curved surface; and calculating the difference between the observed data and the average value of each meteorological site, and taking the calculated difference as the weight of the sampling point corresponding to each meteorological site.
It should be noted that the sampling point weight is one of the super parameters of the HASM method. In the solving process of the traditional HASM method, the weight of each sampling point is manually set according to priori knowledge, and is generally set to be a certain fixed integer value with the value within the range of 1-10, and the default value is 2. In the embodiment of the application, the sampling points are all weather stations, and in order to eliminate the influence caused by abnormal values in the observation data of all weather stations, the difference between the observation data of all weather stations and the average value of the adjacent grid point values of the positions of the corresponding weather stations on the current iteration simulation curved surface is taken as the sampling point weight corresponding to the weather stationθThereby further improving the accuracy of the fusion model.
Based on the construction of the multi-source precipitation fusion model and the parameter optimization, under the optimization control constraint of the precipitation observation value obtained by the meteorological site, the fusion result based on the error characteristic and background knowledge (combined with precipitation influence factors for carrying out geographic weighting) of the multi-source remote sensing precipitation data is used as an initial curved surface of the multi-source precipitation fusion model to drive a numerical simulator of the multi-source precipitation fusion model to carry out iterative solution, and the solution process fuses high-precision meteorological site observation data and the correction result of the multi-source remote sensing precipitation data self-adaptive characteristic fusion data by adopting a GWRR method and the background knowledge, so that the fusion result of the multi-source precipitation data, namely the precipitation space distribution optimal estimated value, is obtained.
Illustratively, referring to fig. 2, the method provided herein may include the steps of: after obtaining a plurality of (e.gKSeed) source remote sensing precipitation data, firstly performing error and variance calculation on the multi-source precipitation data to obtain error characteristics of each precipitation data, then solving weight coefficients of each source precipitation data according to the error characteristics by utilizing a Lagrange multiplier method, and performing self-adaptive characteristic fusion calculation based on the obtained weight coefficients to obtain multi-source remote sensing precipitation dataThe adaptive features of the water data fuse the data. Then adopting a geographic weighted ridge regression method, taking a precipitation influence factor as background knowledge to be fused into the self-adaptive feature fusion data of the multi-source remote sensing precipitation data obtained in the previous step, and further optimizing and downscaling the self-adaptive feature fusion data to obtain a downscaling result of the self-adaptive feature fusion data of the multi-source remote sensing precipitation data; meanwhile, parameter optimization and improvement are carried out on the HASM method by searching radius, setting upper and lower bounds, calculating sampling point observation value weight, adopting a meteorological site observation value as an optimization control condition, and finally, a downscaling result of self-adaptive characteristic fusion data of multi-source remote sensing precipitation data is taken as an initial condition and combined with the improved HASM to construct and obtain the multi-source precipitation fusion model. Through the steps, the precipitation data obtained by high-precision meteorological site observation are utilized to further optimize the precipitation space distribution surface data with high resolution, the simulation result not only can have the precision of the meteorological site data, but also can take into account the regional precipitation distribution outside the meteorological site, the effective fusion of the multi-source precipitation data is realized, and the degree of characterization of precipitation in a research area is enhanced.
In summary, in the application, based on the error characteristics of the multi-source remote sensing precipitation data, the Lagrange multiplier method is utilized to calculate the weight corresponding to the precipitation data; and calculating to obtain self-adaptive feature fusion data based on the weight and the multisource remote sensing precipitation data; then, a geographic weighted ridge regression method is used, and the adaptive feature fusion data of the multi-source remote sensing precipitation data are downscaled by combining the influence factors of precipitation, so that a downscaled result of the adaptive feature fusion data of the multi-source remote sensing precipitation data is obtained; and constructing a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance by combining a high-improved precision curved surface modeling method. According to the method, the weight corresponding to each data source can be adjusted in a self-adaptive mode according to the error characteristics of the fused precipitation data of a plurality of sources, the high-precision simulation advantage of the high-precision curved surface modeling method is fully utilized, a multi-source precipitation fusion model capable of fusing high-dimensional precipitation data is constructed, the model breaks through the limitation that the existing precipitation data fusion model needs to be built on a certain premise assumption, and meanwhile breaks through the limitation that the existing precipitation data fusion model is limited to two to three sources, and the precipitation data of a plurality of sources (three or more) and a plurality of scales can be fused effectively, so that a brand-new and high-precision fusion method is provided for the high-dimensional, multi-source and multi-scale precipitation data.
Exemplary System
The embodiment of the application provides a multisource remote sensing precipitation data self-adaptive fusion system, and a diagram is shown in fig. 3, and the system comprises: a weight calculation unit 301, an adaptive feature fusion unit 302, a data optimization unit 303, and a model construction unit 304. Wherein:
the weight calculation unit 301 is configured to calculate, based on the error characteristics of the multi-source remote sensing precipitation data, a weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data by using a lagrangian multiplier method.
The adaptive feature fusion unit 302 is configured to calculate adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data.
The data optimizing unit 303 is configured to apply a geographic weighted ridge regression method, and combine influence factors of precipitation to downscale the adaptive feature fusion data of the multi-source remote sensing precipitation data, so as to obtain a downscaled result of the adaptive feature fusion data of the multi-source remote sensing precipitation data.
The model construction unit 304 is configured to construct a multi-source precipitation fusion model according to the downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance and by combining an improved high-precision curved surface modeling method.
The multi-source remote sensing precipitation data self-adaptive fusion system provided by the embodiment of the application can realize the steps and the flow of the multi-source remote sensing precipitation data self-adaptive fusion method provided by any embodiment, and achieve the same technical effects, and is not described in detail herein.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. The adaptive fusion method of the multisource remote sensing precipitation data is characterized by comprising the following steps of:
based on error characteristics of multi-source remote sensing precipitation data, calculating and obtaining weight corresponding to precipitation data of each data source in the multi-source remote sensing precipitation data by using a Lagrange multiplier method;
the expression of the error characteristics of the multi-source remote sensing precipitation data is as follows:
wherein:σ 2 is the mean square error;Erepresenting the expected value;urepresenting the actual precipitation data,u i represent the firstiPrecipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source; vSelf-adaptive feature fusion data representing the multi-source remote sensing precipitation data;krepresenting the total number of data sources;
based on the error characteristics of the multi-source remote sensing precipitation data, calculating and obtaining the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data by using a Lagrange multiplier method, wherein the weight is specifically as follows:
solving an expression of the error characteristic of the multi-source remote sensing precipitation data by using a Lagrangian multiplier method to obtain a weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data, wherein the expression of the weight is as follows:
wherein:
、
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source;krepresenting the total number of data sources;
calculating to obtain self-adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data;
performing downscaling on the adaptive feature fusion data of the multi-source remote sensing precipitation data by using a geographic weighted ridge regression method and combining influence factors of precipitation to obtain a downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data;
And constructing a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and the weather site observation data acquired in advance by combining an improved high-precision curved surface modeling method.
2. The adaptive fusion method of multi-source remote sensing precipitation data according to claim 1, wherein the downscaling of the adaptive feature fusion data of the multi-source remote sensing precipitation data is performed by applying a geographic weighted ridge regression method and combining influence factors of precipitation to obtain a downscaling result of the adaptive feature fusion data of the multi-source remote sensing precipitation data, specifically:
according to the following expression:
downscaling the adaptive feature fusion data of the multi-source remote sensing precipitation data;
in the method, in the process of the invention,
a regression function constructed for a geographic weighted ridge regression method;vdata are fused for the self-adaptive characteristics of the multi-source remote sensing precipitation data;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 and merging the downscaling result of the data for the self-adaptive characteristic of the multi-source remote sensing precipitation data.
3. The adaptive fusion method of multi-source remote sensing precipitation data according to claim 2, wherein the expression of the multi-source precipitation fusion model is as follows:
Wherein:A、B、Ccoefficient terms of a finite difference equation set corresponding to the high-precision curved surface modeling method;d、q、pthe right end term of the finite difference equation set corresponding to the high-precision curved surface modeling method is used;x n+1 representing the grid points on the simulated surface corresponding to the high-precision surface modeling methodn+1The value of the secondary iteration;Sis a sampling matrix;gis a sampling vector;
a regression function constructed for a geographic weighted ridge regression method;vdata are fused for the self-adaptive characteristics of the multi-source remote sensing precipitation data;covariateis a covariate set, namely a set formed by influence factors of precipitation;x 0 the self-adaptive characteristics of the multisource remote sensing precipitation data are fused with the downscaling result of the data to serve as a precipitation space distribution initial curved surface corresponding to the current iteration of the high-precision curved surface modeling method;H、Lrespectively the upper and lower boundaries of each grid point on the simulated curved surface corresponding to the high-precision curved surface modeling method, and the regionsThe method is different from the traditional high-precision curved surface modeling method which only uses the constraint of the meteorological site.
4. The adaptive fusion method of multi-source remote sensing precipitation data according to claim 1, wherein the expression of the adaptive feature fusion data of the multi-source remote sensing precipitation data is as follows:
wherein:vself-adaptive feature fusion data representing the multi-source remote sensing precipitation data; 
、
Respectively the firstiData source, the firstjMean square error of precipitation data of the data source;u i represent the firstiPrecipitation data of the data source;krepresenting the total number of data sources.
5. The adaptive fusion method of multi-source remote sensing precipitation data according to claim 3, wherein after constructing a multi-source precipitation fusion model according to a downscaling result of adaptive feature fusion data of the multi-source remote sensing precipitation data and weather site observation data acquired in advance and combining with an improved high-precision curved surface modeling method, the method further comprises:
solving the multisource precipitation fusion model by adopting a pretreatment conjugate gradient method and multiple iteration steps, and continuously adjusting the precipitation space distribution initial curved surface under the optimization control constraint of the meteorological site observation data and the upper and lower boundsx 0 And finally obtaining the optimal estimation value of the precipitation space distribution.
6. The adaptive fusion method of multi-source remote sensing precipitation data according to claim 5, further comprising:
for each iteration, each grid point on the simulated surface is processed as follows:
if no weather station exists in the current grid point, determining the upper and lower boundaries of the current grid point according to the relaxation coefficient of the high-precision curved surface modeling method and the extreme value of the adjacent grid point in the searching radius of the high-precision curved surface modeling method H、L；
Wherein the searching radius is the upper and lower bounds of the current grid point determined by a high-precision curved surface modeling methodH、LThe adjacent grid points to be searched;
if the number of weather stations in the current grid point is less than the preset threshold value, the value on the adjacent grid point in the searching radius is defined as the average value of the observed value of the existing weather stations in the radius and the grid point value of the multi-source remote sensing precipitation data in the searching radius, and the average value is the same asx n+1 Satisfy inequality
。
7. The method for adaptively fusing multi-source remote sensing precipitation data as in claim 5, wherein,
for each iteration, the sampling point weight corresponding to each meteorological site is determined by the following steps:
calculating the average value of adjacent grid point values of the positions of all weather stations on the current iteration simulation curved surface;
and calculating the difference between the observed data of each meteorological site and the average value, and taking the calculated difference as the weight of the sampling point corresponding to each meteorological site.
8. A multi-source remote sensing precipitation data adaptive fusion system, comprising:
the weight calculation unit is configured to calculate and obtain the weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data by utilizing a Lagrange multiplier method based on the error characteristics of the multi-source remote sensing precipitation data;
The expression of the error characteristics of the multi-source remote sensing precipitation data is as follows:
wherein:σ 2 is the mean square error;Erepresenting the expected value;urepresenting the actual precipitation data,u i represent the firstiPrecipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source;vself-adaptive feature fusion data representing the multi-source remote sensing precipitation data;krepresenting the total number of data sources;
the weight calculation unit is further configured to:
solving an expression of the error characteristic of the multi-source remote sensing precipitation data by using a Lagrangian multiplier method to obtain a weight corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data, wherein the expression of the weight is as follows:
wherein:
、
respectively the firstiData source, the firstjMean square error of precipitation data of the data source;ω i represent the firstiWeight corresponding to precipitation data of a data source;krepresenting the total number of data sources;
the self-adaptive feature fusion unit is configured to calculate and obtain self-adaptive feature fusion data of the multi-source remote sensing precipitation data based on the multi-source remote sensing precipitation data and weights corresponding to the precipitation data of each data source in the multi-source remote sensing precipitation data;
the data optimization unit is configured to apply a geographic weighted ridge regression method, and combine influence factors of precipitation to downscale the adaptive feature fusion data of the multi-source remote sensing precipitation data to obtain a downscaled result of the adaptive feature fusion data of the multi-source remote sensing precipitation data;
The model construction unit is configured to construct a multi-source precipitation fusion model according to the downscaling result of the self-adaptive characteristic fusion data of the multi-source remote sensing precipitation data and weather site observation data acquired in advance and an improved high-precision curved surface modeling method.
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